ACCEL is a general purpose system that uses abductive reasoning to construct explanations for observed intelligent phenomena. These explanations are then used to avoid redundant work in future problem solving episodes. We define an abductive explanation as a consistent set of assumptions which when combined with background knowledge, logically entails a set of observations.
ACCEL has been constructed as a domain-independent system, in which knowledge about a variety of domains has been uniformly encoded as first-order Horn-clause axioms. A general-purpose abduction algorithm, AAA, is used to efficiently construct explanations by caching partial explanations. ACCEL has been shown to achieve more than an order of magnitude speedup in run time for a variety of domains, including plan recognition in text understanding, and diagnosis of medical diseases, logic circuits, and dynamic systems.
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